Creativity: Generating Diverse Questions using Variational Autoencoders

Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. Different from many conventional prediction techniques is the need for algorithms to generate a diverse set of plausible questions, which we refer to as "creativity". In this paper we propose a creative algorithm for visual question generation which combines the advantages of variational autoencoders with long short-term memory networks. We demonstrate that our framework is able to generate a large set of varying questions given a single input image.Comment: Accepted to CVPR 201

SALSA-TEXT : self attentive latent space based adversarial text generation

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code- based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to their promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.Comment: 10 pages, 3 figures, under review at ICLR 201

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Make-A-Story: Visual Memory Conditioned Consistent Story Generation

There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for more complex task of story visualization, where naturally references and co-references exist, and one requires to reason about when to maintain consistency of actors and backgrounds across frames/scenes, and when not to, based on story progression, remains a challenge. In this work, we address the aforementioned challenges and propose a novel autoregressive diffusion-based framework with a visual memory module that implicitly captures the actor and background context across the generated frames. Sentence-conditioned soft attention over the memories enables effective reference resolution and learns to maintain scene and actor consistency when needed. To validate the effectiveness of our approach, we extend the MUGEN dataset and introduce additional characters, backgrounds and referencing in multi-sentence storylines. Our experiments for story generation on the MUGEN and the FlintstonesSV dataset show that our method not only outperforms prior state-of-the-art in generating frames with high visual quality, which are consistent with the story, but also models appropriate correspondences between the characters and the background.Comment: 10 page

Multimodal Representation Learning for Visual Reasoning and Text-to-Image Translation

abstract: Multimodal Representation Learning is a multi-disciplinary research field which aims to integrate information from multiple communicative modalities in a meaningful manner to help solve some downstream task. These modalities can be visual, acoustic, linguistic, haptic etc. The interpretation of ’meaningful integration of information from different modalities’ remains modality and task dependent. The downstream task can range from understanding one modality in the presence of information from other modalities, to that of translating input from one modality to another. In this thesis the utility of multimodal representation learning for understanding one modality vis-à-vis Image Understanding for Visual Reasoning given corresponding information in other modalities, as well as translating from one modality to the other, specifically, Text to Image Translation was investigated. Visual Reasoning has been an active area of research in computer vision. It encompasses advanced image processing and artificial intelligence techniques to locate, characterize and recognize objects, regions and their attributes in the image in order to comprehend the image itself. One way of building a visual reasoning system is to ask the system to answer questions about the image that requires attribute identification, counting, comparison, multi-step attention, and reasoning. An intelligent system is thought to have a proper grasp of the image if it can answer said questions correctly and provide a valid reasoning for the given answers. In this work how a system can be built by learning a multimodal representation between the stated image and the questions was investigated. Also, how background knowledge, specifically scene-graph information, if available, can be incorporated into existing image understanding models was demonstrated. Multimodal learning provides an intuitive way of learning a joint representation between different modalities. Such a joint representation can be used to translate from one modality to the other. It also gives way to learning a shared representation between these varied modalities and allows to provide meaning to what this shared representation should capture. In this work, using the surrogate task of text to image translation, neural network based architectures to learn a shared representation between these two modalities was investigated. Also, the ability that such a shared representation is capable of capturing parts of different modalities that are equivalent in some sense is proposed. Specifically, given an image and a semantic description of certain objects present in the image, a shared representation between the text and the image modality capable of capturing parts of the image being mentioned in the text was demonstrated. Such a capability was showcased on a publicly available dataset.Dissertation/ThesisMasters Thesis Computer Engineering 201